The European Central Bank (ECB) has announced that it is exploring technology similar to ChatGPT.

The central bank said that large-language models, the technology behind the chatbot, could be used to write initial drafts of code for experts for use in analysis, or test software more quickly and thoroughly.

It added that these models could be used to analyse, summarise and compare the documents prepared by the banks it supervises.

“The technology is also capable of helping to more quickly prepare summaries and draft briefings, which can assist colleagues across the bank in policy and decision-making activities,” said Myriam Moufakkir, chief services officer at the ECB. “A large language model can also help improve texts being written by staff members, making the ECB’s communication easier to understand for the public.”

She added that the central bank has been using neural network machine translations for some time to help it communicate with European citizens in their mother tongues.

The announcement as the ECB launches a new “SupTech hub” to explore the potential use cases for AI in the context of banking supervision and foster collaboration.

The central bank says that the data it uses, the manual classification of which is “very time-consuming”, was a key focus in its thinking around AI and related technology deployments.

“Machine learning techniques allow us to automate the classification process, meaning that our staff can focus on assessing and interpreting these data,” it said.

As a means of developing its understanding of price-setting behaviour and inflation dynamics in the EU, it added that by applying web scraping and machine learning, it is able to assemble a “huge amount of real-time data on individual product prices.”

With the creation of the hub, the central bank shared that it had already launched several AI-related projects: one on using natural language processing to improve the search function for unstructured information, and another to introduce machine learning to certain work-intensive processes.

Citing one example, the ECB said with AI, supervisors could “draw deeper insights” from a wide range of data types and make “more informed, data-driven decisions.”

It added that AI can also identify patterns that humans fail to spot and thereby enhance the quality of supervision.

The ECB also stated that the hub would connect internal and external stakeholders and help to support colleagues in using advanced analytics and developing new tools, and that it had already reached out to a number of peer authorities and academic institutions worldwide to exchange AI expertise and practices.

It added that it had begun to develop new capabilities in the European supervisory community through training and workshops.

The central bank concluded: “The ECB will continue, with caution, to explore new technologies and introduce AI into its supervisory processes in order to deliver high-quality supervision.”

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